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This article is about how to use Nvivo to build computing ontology, the editor feels very practical, so share with you to learn, I hope you can learn something after reading this article, say no more, follow the editor to have a look.

NVivo is a powerful qualitative and hybrid data analysis tool that can help you easily organize and analyze disordered information, such as collecting, collating and analyzing interviews, focus group discussions, questionnaires, audio, and so on. NVivo allows you to make better decisions in the end.

The following mainly describes how to use the coding of nodes and relationships to help develop computational ontologies without losing richness and nuance.

Developments in the field of computing provide plenty of opportunities for researchers, such as more complex correspondence analysis of large statistical data sets than ever before, and automatic transcription of interview records. These advances have been accepted in the study of digital humanities. For example, in the project Beyond the Multiplex (UKRI,2017), it is described that a computing ontology is being developed to explore data from a large-scale hybrid methodology research project that includes the following data:

Longitudinal survey: 3 times within 6 months (N = 5000 ~ n = 500 ~ 500)

Semi-structured interviews with audience members (x 200)

Expert interviews with policy makers (x 32)

Film-inspired focus group with audience participation (x 16)

Policy document (+ 250)

Computational ontology allows researchers to classify "components and characteristics of specific areas of knowledge" as "entities", "characteristics of entities", or as "relationships" between two classes. Instead of using the classification of these three parts to determine how the data describes the structure and related information. In the structure mentioned above, computational ontology enables researchers to specify exactly how entities relate to each other.

In our project, we study professional moviegoers and their film observation practices, linking national policy with industry practice. We use computing ontology to explore the data in the project as a whole, and query all data types, in order to analyze the development of the concept formed in the interview by comparing the survey data with national policy. To this end, ontology and the widespread use of Nvivo relationship types provide a way to aggregate concepts developed in independent analysis (separate NVivo projects) and explore how they relate to each other.

Typically, software developers use data models and computational ontologies to provide data structures. The structure is imposed on the data, and any subsequent data is adjusted to accommodate the pre-existing structure. This is a process full of personal assumptions, discrimination and prejudice. In contrast, we encode the relationship in NVivo and name the relationship type to fundamentally (inductively) establish a structure. This ensures that when we develop a computing ontology it is still data-based and data-driven.

To develop the computing ontology, we first use NVivo to write copies of interviews and focus groups. We code as nodes (for developing entities and entity features), and then we establish (and encode) their relationships and assign them to a set of relationship types that we develop throughout the coding process.

Whether you are doing small-scale qualitative analysis or building a computational ontology from a large mixed method dataset, coding relationships or relationship types in Nvivo provides a useful way to explore how a project (such as a node) in your project is connected to another. It is relatively easy to create relationships and relationship types:

Step 1

Select Create from the function area bar, and then select Relationships in Nodes Group.

Step 2

When the dialog box pops up, use two Select buttons to access the second dialog box. This allows you to search for and select two project items to connect in the new relationship.

Tip: when you create a new relationship, the relationship type is specified as Associated and not assigned to any specific direction. If you want to specify a relationship as a specific type, simply follow the third step below.

Step 3

In the dialog box described in step 2, select the New button. This opens an additional dialog box that allows you to create a relationship type and define its direction. For example, when we look at people's choice of movie viewing platforms, we find that video-on-demand services such as Amazon Prime and Netflix are beginning to replace domestic DVD collections, but the opposite is true. To do this, we created a new relationship called REPLACES, which connects an entity named Video-on-Demand Services to an entity feature named DVD Collection.

In order to encode the data, we draw a set of initial high-level nodes (entities and features), such as Times, Places, and so on, before extending them through data analysis. Here, the initial entity set and entity characteristics of the ontology are defined. By using NVivo in this way, we find that we can work inductively rather than forcing to encode data to this structure. And this is closer to our data, while maintaining consistency between data sets.

Converting nodes, relationships, and relationship types to ontologies requires some follow-up work with NVivo. For example, we run Extracts in NVivo to get the XML file that encodes all the text as nodes and to identify all intersecting nodes. We also export all relationships (and relationship types) to HTML files. After parsing the extraction and export using XML, we use Javascript to prepare and build them into the SQL-based database and the computational ontology itself.

In general, by using NVivo to encode our data and build the coding scheme, we can provide analysis suitable for computing ontology. This enables us to go beyond the traditional hybrid approach and use a large amount of empirical data without imposing pre-conceived ideas on the research itself.

The above is how to use Nvivo to build computing ontology. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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